DEMO QUESTIONS

Q1:

What does price elasticity measure in the context of pricing strategies?

A: The relationship between product quality and customer satisfaction

B: How seasonal trends affect customer demand

C: The impact of advertising on sales volume

D: How sensitive customer demand is to price changes

Correct Answer: D

The CPCM pricing analytics course covers advanced analytic techniques used to assess retailer pricing, including price-setting rules and methods used to evaluate pricing decisions. Price elasticity is one of the core pricing analytics concepts because it measures how demand responds when price changes. Harvard Business Review defines price elasticity as showing how responsive customer demand is for a product based on its price.

Option D is the only answer that correctly describes price elasticity. It is about demand sensitivity to price changes.

Option A is wrong because product quality and satisfaction are consumer perception measures. Option B is seasonality analysis. Option C is advertising or promotion response analysis. None of those define price elasticity.

Q2:

When showing the size of prize, what factors are good to keep in mind?

A: Don't show the math, not necessary.

B: Be sure to include comprehensive analytics.

C: Make sure it's reasonable and show the math on how to achieve the plan.

D: Make sure it's a high enough number to get their attention.

Correct Answer: C

The ”size of prize” must be credible. In category management, it is not enough to show a large opportunity number just to impress the buyer. The opportunity should be reasonable, tied to facts, and supported by clear math. CMKG’s fact-based presentation guidance specifically emphasizes defining the growth opportunity, quantifying the opportunity, identifying the strategy, and creating action with tactics. It also says presentations should include relevant insights derived from category data to support the idea.

Option A is wrong because hiding the math weakens trust. Option B is too broad because ”comprehensive analytics” can become overwhelming if it is not focused. Option D is dangerous because inflating the opportunity just to get attention undermines credibility. A strong size-of-prize statement should make the buyer think: ”That number is realistic, the logic is clear, and the path to achieving it makes sense.”

Q3:

What does the metric ‘Household Penetration’ measure in market-level shopper dynamics?

A: The average number of products purchased by each household within a specific category over a given timeframe.

B: The proportion of a household's total spending allocated to a specific product category.

C: The total revenue generated by a specific product category across all households in a market.

D: The percentage of households within a defined group or market that have purchased a specific product category within a given timeframe.

Correct Answer: D

Household penetration measures how many households bought the product, brand, category, or product group during the measured period. CMKG explains the panel-data formula as Total Number of Buying Households, or Penetration, multiplied by Spend per Buying Household equals Dollar Sales. It further explains that penetration relates to the number of households purchasing the product.

Option A describes purchase quantity or items per household, not penetration. Option B describes share of wallet or share of requirements-type spending allocation, not household penetration. Option C describes category dollar sales, not the breadth of the buyer base.

Household penetration is a reach measure. It tells whether the category is bought by many households or only by a narrow group of households.

Q4:

The best Predictive Analytic tools use which of the following? Select the best answer.

A: Historical Data, Statistical Models and Machine Learning

B: Historical Data and Statistical Models

C: Historical Data and Machine Learning

D: Statistical Models and Machine Learning

Correct Answer: A

The CPCM course states that moving into advanced category analytics includes predictive analytics, specifically naming collaborative filtering, clustering algorithms, regression models, and time-to-event models. Those methods require historical data, statistical modeling, and machine-learning-style pattern recognition. IBM defines predictive analytics as predicting future outcomes by using historical data combined with statistical modeling, data mining techniques, and machine learning.

Option A is the most complete answer because predictive analytics needs all three: historical data to learn from, statistical models to quantify relationships, and machine learning to detect patterns and improve prediction. Option B omits machine learning. Option C omits statistical models. Option D omits historical data, which is the base input for predictive analytics.

Q5:

What is the primary goal of SKU rationalization in supply chain management?

A: To increase the number of products available to customers

B: To eliminate all high-cost products from the inventory

C: To focus solely on high-demand seasonal products

D: To reduce complexity by removing slow-moving and redundant products

Correct Answer: D

SKU rationalization is about improving the product mix by removing or consolidating items that create unnecessary complexity without contributing enough value. The CPCM course includes Efficient Assortment and Retailer Economics and the Product Supply Chain, which means assortment decisions are not only shopper-facing; they also affect inventory, operations, cost, and execution. The CMKG supply-chain material states that product supply chain affects ”inventory, forecasting, availability, cash flow, service levels, and ultimately the shopper experience.”

Option D is the only answer that reflects the real supply-chain objective: reduce operational complexity by removing slow-moving, duplicated, or redundant SKUs. Option A is the opposite; adding more products can increase complexity. Option B is too aggressive because high-cost products may still be profitable or strategically important. Option C is too narrow because SKU rationalization is not only about seasonal demand.

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